'OPTImAL': an ontology for patient adherence modeling in physical activity domain

Autor: Vassilis Koutkias, Evangelia Kouidi, Nikolaos Maglaveras, Mark van Gils, Ioanna Chouvarda, Kristina Livitckaia
Jazyk: angličtina
Předmět:
Male
Knowledge management
020205 medical informatics
media_common.quotation_subject
Health Behavior
Physical fitness
Patient adherence
Psychological intervention
Health Informatics
02 engineering and technology
Ontology (information science)
lcsh:Computer applications to medicine. Medical informatics
Health informatics
Research data modeling
Knowledge base
03 medical and health sciences
0302 clinical medicine
SDG 3 - Good Health and Well-being
0202 electrical engineering
electronic engineering
information engineering
Humans
Quality (business)
030212 general & internal medicine
Health behavior
Exercise
media_common
Cardiac Rehabilitation
Ontology
Physical activity
business.industry
Health Policy
Rehabilitation
Models
Theoretical
Cardiovascular disease
3. Good health
Computer Science Applications
Knowledge sharing
Cardiovascular Diseases
lcsh:R858-859.7
Patient Compliance
Domain knowledge
Female
business
Psychology
Zdroj: Livitckaia, K, Koutkias, V, Kouidi, E, van Gils, M, Maglaveras, N & Chouvarda, I 2019, ' "OPTImAL" : An ontology for patient adherence modeling in physical activity domain ', BMC Medical Informatics and Decision Making, vol. 19, no. 1, 92 . https://doi.org/10.1186/s12911-019-0809-9
BMC Medical Informatics and Decision Making
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-15 (2019)
ISSN: 1472-6947
DOI: 10.1186/s12911-019-0809-9
Popis: Background: Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exercise, both in daily life and during cardiac rehabilitation (CR), is low. Patient adherence is frequently composed of numerous determinants associated with different patient aspects (e.g., psychological, clinical, etc.). Understanding the influence of such determinants is a central component of developing personalized interventions to improve or maintain patient adherence. Medical research produced evidence regarding factors affecting patients' adherence to physical activity regimen. However, the heterogeneity of the available data is a significant challenge for knowledge reusability. Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse. In this paper, we are proposing an ontology called OPTImAL, focusing on CVD patient adherence to physical activity and exercise training. Methods: OPTImAL was developed following the Ontology Development 101 methodology and refined based on the NeOn framework. First, we defined the ontology specification (i.e., purpose, scope, target users, etc.). Then, we elicited domain knowledge based on the published studies. Further, the model was conceptualized, formalized and implemented, while the developed ontology was validated for its consistency. An independent cardiologist and three CR trainers evaluated the ontology for its appropriateness and usefulness. Results: We developed a formal model that includes 142 classes, ten object properties, and 371 individuals, that describes the relations of different factors of CVD patient profile to adherence and adherence quality, as well as the associated types and dimensions of physical activity and exercise. 2637 logical axioms were constructed to comprise the overall concepts that the ontology defines. The ontology was successfully validated for its consistency and preliminary evaluated for its appropriateness and usefulness in medical practice. Conclusions: OPTImAL describes relations of 320 factors originated from 60 multidimensional aspects (e.g., social, clinical, psychological, etc.) affecting CVD patient adherence to physical activity and exercise. The formal model is evidence-based and can serve as a knowledge tool in the practice of cardiac rehabilitation experts, supporting the process of activity regimen recommendation for better patient adherence.
Databáze: OpenAIRE
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